This week I spent time with the team talking more about the details for implementation. I spent some time investigating and researching how we were planning on detecting obstacles from our image. I figured that edge detection would be the most lightweight and functional tool, so I started looking at frameworks that support edge detection. Some of the libraries that I found were OpenCV, a popular computer vision framework, and PyTorch with TorchVision. These both have a lot of exciting documentation and examples of how to use them. I can see these being very helpful in creating the 2d occupancy array that we can later run a pathfinding algo on, like D-star. I also found a somewhat robust library for object recognition called YOLO. Although YOLO doesn’t have the greatest accuracy for everyday objects (~57%), the underlying model should be helpful in our use case as there isn’t such a wide variety of objects that are seen in indoor shared spaces.
For the next week, we are going to be able to pick up our camera that we ordered and I want to start experimenting with the camera and seeing what kind of recognition/detection results we can get. This will probably require some set up time to get the camera to work with my laptop, and further work to get it to work with the NVIDIA Jetson we plan to use.